> For the complete documentation index, see [llms.txt](https://docs.aporia.com/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.aporia.com/data-sources/big-query.md).
# BigQuery
This guide describes how to connect Aporia to a BigQuery data source in order to monitor your ML Model in production.
We will assume that your model inputs, outputs and optionally delayed actuals can be queried with SQL. This data source may also be used to connect to your model's training set to be used as a baseline for model monitoring.
### Create a materialization dataset for Aporia queries
Create a materialization dataset for Aporia to use to perform queries, see instructions [here](https://cloud.google.com/bigquery/docs/datasets#create-dataset).
A separate materialization dataset location, to which query results will be written, must be designated for each project from which you want to query.
### Update the Aporia Service Account for BigQuery access
In order to provide access to BigQuery, you'll need to update your Aporia service account with the necessary API permissions.
#### Step 1: Obtain your aporia service account
Use the same service account used for the Aporia deployment. If someone else on your team has deployed Aporia, please reach out to them to obtain it.
#### Step 2: Grant read access to the relevant project
1. Go to the [IAM console](https://console.cloud.google.com/iam-admin/) and login.
2. Find the Aporia service account you obtain in the previous step and click on 🖋 **Edit Principle**
3. In the "Edit access" window click on **ADD ANOTHER ROLE**
4. Add the `BigQuery Data Viewer` and `BigQuery Job User` roles and click **Save**
#### Step 3: Grant access to the materialization dataset
1. Go to the [BigQuery console](https://console.cloud.google.com/bigquery) and login.
2. In the left-hand panel, expand the relevant project and find the materialization dataset you created in the previous steps.
3. Click on "**...**" by the dataset name, then click on **Share**
4. In the "Share permissions" window click on **Add Principal**
5. In the "New principal" box, enter the email of the Aporia service account you have obtained. Choose the `BigQuery Data Editor` role and click **Save**.
Now Aporia has the permission it needs to connect to the BigQuery datasets and tables you have specified in the policy.
### Create a BigQuery data source in Aporia
1. Go to [Aporia platform](https://platform.aporia.com/) and login to your account.
2. Go to **Integrations** page and click on the **Data Connectors** tab
3. Scroll to **Connect New Data Source** section
4. Click **Connect** on the BigQuery card and follow the instructions
Bravo! :clap: now you can use the data source you've created across all your models in Aporia.
---
# Agent Instructions
This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. Learn more at gitbook.com.
## Querying This Documentation
If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question.
Perform an HTTP GET request on the current page URL with the `ask` query parameter, and the optional `goal` query parameter:
```
GET https://docs.aporia.com/data-sources/big-query.md?ask=&goal=
```
`ask` is the immediate question: it should be specific, self-contained, and written in natural language.
`goal` is optional and describes the broader end goal you are ultimately trying to accomplish on behalf of the user. GitBook uses it to tailor the answer towards what is most useful for that goal.
The response will contain a direct answer to the question and relevant excerpts and sources from the documentation.
Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.